AI risk management in critical infrastructure via NIST AI RMF 1.0
How critical infrastructure operators can adapt the NIST AI RMF 1.0 framework to defend against specific AI threats and integrate it into corporate cybersecurity.
EXPERT / Euro Consulting
Automation Practice Lead
Euro Consulting editorial profile. Focus: automation practice lead. Publications cover business-process governance, internal controls and digital change. This profile uses an editorial name.
How critical infrastructure operators can adapt the NIST AI RMF 1.0 framework to defend against specific AI threats and integrate it into corporate cybersecurity.
How to protect corporate LLM integrations from data leaks and new attack vectors using NIST AI RMF, MITRE ATT&CK frameworks, and Security by Design architecture.
A practical approach to overcoming SOC alert fatigue through MITRE ATT&CK mapping, SOAR automation, and NIST CSF 2.0 business prioritization.
A critical zero-day vulnerability, CVE-2026-35273, in Oracle PeopleSoft has been exploited by the ShinyHunters group to compromise educational institutions, highlighting the ongoing risks associated with legacy ERP systems.
CERT-UA has reported on new tooling used by the UAC-0057 group. We examine the technical aspects of OYSTERFRESH, OYSTERSHUCK, and OYSTERBLUES, along with practical recommendations for defense.
CERT-UA warns of intensified cyberattacks targeting critical infrastructure. We examine new attacker tactics and effective defense strategies based on Zero Trust architecture.
The Miasma attack on Red Hat's npm packages highlights supply chain risks and credential theft, which are critical for AI integration security.
Protecting AI services from prompt injection and data leaks in 2026 requires a comprehensive approach and integration with existing cybersecurity tools.
The increasing number of cyberattacks on critical infrastructure necessitates a reevaluation of approaches to securing IoT and SCADA systems. Emerging challenges for 2026 demand integrated solutions and continuous monitoring.
By 2026, over 70% of new AI systems will experience data breaches due to inadequate Data Loss Prevention (DLP) integration, necessitating a reevaluation of sensitive information protection in the age of artificial intelligence.
According to analytical reports, by 2026, up to 70% of corporate data breaches will be linked to insufficiently effective employee offboarding processes. This highlights the critical need to review and strengthen access management policies after termination.
The rise of hybrid and remote work models significantly complicates the task of protecting corporate content. By 2026, effective DLP strategies must adapt to these changes, focusing on preventative measures and automation.